Top 5 AI APIs Every Front-End Developer Needs in Their Stack

Q: OpenAI or Anthropic?

Default to OpenAI GPT-4o-mini for general chat, summarisation, and classification — cheaper and faster. Switch to Anthropic Claude 3.5 Sonnet when your task needs to read a >50k token document at once, or when extraction accuracy matters more than latency. Most apps end up using both for different features, not one or the other.

7 min read · Updated Jun 4, 2026

You can ship serious AI features into a web app today with five API keys and ~200 lines of integration code. This guide picks the five APIs I actually keep in my front-end stack in 2025 — with the gotcha each one carries, the realistic cost at small-app scale, and the one I always reach for first when a new client asks for "AI". The list is opinionated. Most "top AI APIs" round-ups are flat catalogues; this one tells you what to ship in what order.

Key takeaways

Start with OpenAI (chat) + Vercel AI SDK — ships in an afternoon, covers 70% of real product needs.
Add embeddings + pgvector (Cohere or OpenAI) as the second integration — turns any existing search bar into semantic search.
Anthropic Claude wins for long-document workloads (200k context); cheaper than GPT-4o for the same accuracy on extraction tasks.
Replicate is the right call for image/video generation — don’t run Stable Diffusion on your own GPU until you’re spending >$500/mo on Replicate.
Deepgram beats Whisper-API for real-time transcription; Whisper wins for batch transcription on cost.

The five APIs that actually earn their keep

My 2025 default front-end AI API stack.
#	API	What I use it for	Realistic cost (small app)	The gotcha
1	OpenAI (GPT-4o-mini)	Chat, summarisation, structured extraction	$20–100/mo	Without abort propagation you pay for streams users abandon
2	OpenAI Embeddings (text-embedding-3-small)	Semantic search, RAG, similar-items	$5–20/mo at 1M tokens	Re-embed when you switch model versions or your search silently degrades
3	Anthropic Claude 3.5 Sonnet	Long-doc extraction, complex reasoning	$30–150/mo	Rate limits much tighter than OpenAI on free/low tiers
4	Replicate (Flux, Stable Diffusion, Whisper)	Image gen, background removal, batch transcription	$10–100/mo at typical demo loads	Cold start can be 30s+ on rarely-used models — keep one warm if UX-critical
5	Deepgram (Nova-2)	Live transcription, voice input	$15–80/mo	WebSocket connection limits on lower tiers — check before launching

1. OpenAI — the workhorse

GPT-4o-mini at $0.15/M input + $0.60/M output is the default for chat, summarisation, classification, and structured extraction. It handles ~95% of what people ask "an AI" to do in a web app. The Vercel AI SDK’s useChat hook puts a streaming chat UI in your app in ~10 lines. Reach for GPT-4o (the big sibling) only when 4o-mini fails on your specific eval; the cost difference is 17x.

2. OpenAI Embeddings + pgvector — the silent superpower

Embeddings convert text into 1536-dim vectors that capture meaning. Combined with Postgres + the pgvector extension, you can replace any keyword search with a semantic one in an afternoon. text-embedding-3-small costs $0.02 per million tokens — embedding a 50k-row product catalogue is <$1. The upgrade in user-facing quality (no more "no results" because the user typed "shoes" not "footwear") is the single most under-appreciated AI win in front-end work.

typescript

// Minimal semantic search route
import OpenAI from "openai";
import { sql } from "@vercel/postgres";

const openai = new OpenAI();

export async function searchProducts(query: string) {
  const { data: [{ embedding }] } = await openai.embeddings.create({
    model: "text-embedding-3-small",
    input: query,
  });
  // pgvector <-> is cosine distance
  const { rows } = await sql`
    SELECT id, name, description
    FROM products
    ORDER BY embedding <-> ${JSON.stringify(embedding)}::vector
    LIMIT 10
  `;
  return rows;
}

3. Anthropic Claude — when context window matters

Claude 3.5 Sonnet’s 200k context window means you can feed an entire 300-page PDF, a complete codebase, or six months of customer conversations into a single call. For extraction tasks ("pull every clause referencing data retention from this contract"), Sonnet is consistently more accurate than GPT-4o-mini and ~30% cheaper than GPT-4o for the same task. Reach for Claude when the answer requires reading the whole document at once; reach for OpenAI when chunking + RAG is acceptable.

4. Replicate — outsourced GPUs

Replicate hosts thousands of open-source models behind a uniform API. Flux for high-quality image generation, Real-ESRGAN for upscaling, rembg for background removal, Whisper-large for batch transcription. Pay-per-second pricing means an app generating ~100 images/day costs <$10/month. The downside: cold start. A rarely-used model can take 30+ seconds to spin up. For UX-critical paths, set keep_warm or pay the cold-start premium upfront.

5. Deepgram — the streaming transcription pick

For real-time voice input (browser microphone → live transcript), Deepgram’s Nova-2 is faster and cheaper than OpenAI’s Whisper API and has a proper WebSocket protocol. ~300ms median latency, accurate on accents and technical jargon. For batch transcription of recorded files (podcasts, meeting recordings) where latency doesn’t matter, Whisper via Replicate is cheaper. Choose by use case, not brand loyalty.

The story that taught me the API choice barely matters

November 2024, Wednesday afternoon. A founder I was advising — 8-person legaltech, 18-month-old product — messaged me a 1,400-word doc titled "AI Strategy 2025" listing 14 APIs they were evaluating: OpenAI, Anthropic, Gemini, Mistral, Cohere, Replicate, Together, HuggingFace, Voyage, Jina, Stability, Runway, Deepgram, AssemblyAI. They’d been comparing for six weeks. Their product had zero AI features shipped. I read it, called him, said "ship OpenAI behind a feature flag on Monday, run it for two weeks, then decide." He pushed back — "but Anthropic is better at long context." Sure. But their actual users were asking for chat summarisation of <5,000-token conversations. GPT-4o-mini was overkill for that. They shipped on the Tuesday. Two weeks later: 31% chat-summary usage, 4.6/5 satisfaction, zero user complaints about model quality. The bottleneck was never the model — it was the absence of the feature. Eight months later they still use OpenAI for chat, added Anthropic only for one specific contract-review feature where 200k context genuinely matters, and added pgvector + embeddings for search. Three APIs across the entire product. The 14-API analysis was procrastination dressed as research. Ship one, measure for two weeks, then decide whether you need the others.

A 4-week shipping sequence

Week 1: OpenAI GPT-4o-mini chat behind a feature flag, Vercel AI SDK useChat, rate-limited route handler, abort propagation. Ship.
Week 2: Embed your existing primary searchable entity (products, articles, customers) with text-embedding-3-small + pgvector. Wire it into your existing search bar.
Week 3: Add ONE non-text capability your product actually needs. Voice input (Deepgram), image generation (Replicate), or long-doc extraction (Anthropic). Pick one. Ship it.
Week 4: Add observability: per-request token logging, per-user cost tracking, abort-rate metric. Without this you can’t tune anything.

The opinion I will defend

“The best AI API is the one you ship on Monday and measure on Friday — not the one you’re still evaluating in six weeks.”

Frequently asked questions

OpenAI or Anthropic?

Default to OpenAI GPT-4o-mini for general chat, summarisation, and classification — cheaper and faster. Switch to Anthropic Claude 3.5 Sonnet when your task needs to read a >50k token document at once, or when extraction accuracy matters more than latency. Most apps end up using both for different features, not one or the other.

Should I self-host an open-source model instead?

Not at small-to-mid scale. The break-even vs OpenAI GPT-4o-mini on a self-hosted GPU (e.g., Llama 3 70B on a Hetzner GPU instance) is around $400/month of OpenAI spend, and that’s before you account for the engineering time to maintain it. Self-host when you have compliance constraints (data can’t leave your VPC) or when you’re spending >$1k/month on hosted inference.

How do I keep API costs under control?

Three things, in order: (1) log token usage per request to Postgres with user_id; (2) cap max_tokens server-side; (3) propagate AbortSignal from the client to the model. The cost issues teams have are almost always one of these missing, not "we picked the expensive model."

What about Google Gemini?

Gemini 2.0 Flash is genuinely competitive on price and quality, especially for multimodal (text+image) tasks. The reason it’s not on my default list: the SDK ergonomics and streaming protocol are slightly worse than OpenAI/Anthropic, and the failure mode of the model on edge cases is harder to debug. Solid pick if you’re already on Google Cloud.

Do I need vector search if I have a good keyword search?

You need it the first time a user types "shoes" and gets no results because your catalogue uses "footwear." Embeddings + pgvector closes that gap for under $20/month on most app sizes. The user-facing quality jump is bigger than any chat feature you can add.

How do I evaluate which model is best for my task?

Build an "eval set" of 30–50 representative inputs with the correct or preferred output. Run each candidate model against it. Score automated where you can (string match, JSON validity, semantic similarity vs reference) and human-score the rest. This 2–3 day investment beats 6 weeks of vendor comparison reading.